For resources that have dynamically varying metrics relating to their use, such resources may be difficult if not impossible to allocate efficiently. Various resource-allocation problems may exist in complex systems with dynamically fluctuating resources. An example of such resources is cloud-computing infrastructure. Many large-scale deployments of cloud-computing services may include petabytes to exabytes of distributed data storage, as well as many teraFLOPS to petaFLOPS of distributed computing capabilities across thousands of nodes. The resulting complexity of this scale makes monitoring and prediction of cloud-computed resource metrics (e.g., capacity, cost, efficiency) burdensome or inaccurate, if not infeasible.
A cloud administrator, or automated administration tool, lacking access to information of contemporaneous operating conditions for multiple infrastructure options may in turn have difficulty making decisions of where to deploy or migrate computing services across those multiple infrastructure options. Reliance on old information that is no longer accurate and/or relying on ill-informed speculation about future metrics, may involve substantial risk of suboptimal performance of the computing services where projected future metrics deviate from actual values as time progresses.
In the drawings, like reference numbers generally indicate identical or similar elements. Additionally, generally, the left-most digit(s) of a reference number identifies the drawing in which the reference number first appears.